Gender price gaps and competition: Evidence from a correspondence study

Last registered on December 07, 2018

View Trial History

Pre-Trial

Trial Information

General Information

Title

Gender price gaps and competition: Evidence from a correspondence study

RCT ID

AEARCTR-0003279

Initial registration date

August 30, 2018

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

August 31, 2018, 1:11 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

December 07, 2018, 10:58 AM EST

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Margarita Machelett

Affiliation

Banco de España

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2018-05-01

End date

2018-12-21

Keywords

Gender

Additional Keywords

Pricing, discrimination, field experiment, gender

JEL code(s)

Secondary IDs

Abstract

This studies gender-based price discrimination in service markets. It explores if additional information about customers can close these gaps to shed light between statistical discrimination and taste-based discrimination motives. Importantly, it combines experimental results with detailed information about competition measures in this market.

External Link(s)

Registration Citation

Citation

Machelett, Margarita. 2018. "Gender price gaps and competition: Evidence from a correspondence study." AEA RCT Registry. December 07. https://doi.org/10.1257/rct.3279-3.0

Former Citation

Machelett, Margarita. 2018. "Gender price gaps and competition: Evidence from a correspondence study." AEA RCT Registry. December 07. https://www.socialscienceregistry.org/trials/3279/history/38567

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2018-07-02

Intervention End Date

2018-11-30

Primary Outcomes

Primary Outcomes (end points)

total price estimates

Primary Outcomes (explanation)

The primary variable of interest is the price estimate received by each gender and customer type.
Prices will be defined using the following rules:
1) Use the total price provided in the estimate
2) Use the average price if price ranges are provided,
3) Use the price after discounts, whenever discounts are offered,
4) Use the first price provided by a shop, whenever the same shop provides more than one price
in separate emails
5) Drop prices when either the labor or radiator price are explicitly excluded in the estimate,
6) Drop extremely low or high prices, below $100 and above $2,000, and estimates where the
price range provided is so wide that the maximum price is more than twice as large as the minimum
and price composition is not given. These thresholds are arbitrary but conservative values. I will
add robustness exercises varying these thresholds.
7) Use price provided by the shop, whenever price matching is offered. In alternative specifications,
I will use the price picked from a random draw of lower nearby prices obtained by the same
user and customer type.

Secondary Outcomes

Secondary Outcomes (end points)

The proportion of emails replied, the proportion of estimates given, the proportion of estimates given on first email reply, price composition

Secondary Outcomes (explanation)

I will include indicator variables for receiving a shop reply, replying to a shop once before receiving a price and receiving a price estimate. I will use these variables to compare response rates and effort it takes to obtain prices. Shops often provide detail on the total estimate composition. I will keep track of included and explicitly not included parts, along with their prices and quantities. I can potentially explore if
shops tend to recommend overtreatment and how price components vary with customer characteristics.

Experimental Design

The experimental design uses a correspondence-study approach.

Experimental Design Details

The experimental design uses a correspondence-study approach, in which I send emails to shops varying the perceived gender and customer information revealed to each shop.

Randomization Method

Randomization done by computer

Randomization Unit

The first experimental design is simple randomization.
The second experimental design is at the shop level, sending a second email to the same shop.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Approximately 60,000 shops

Sample size: planned number of observations

Approximately 60,000 shops.

Sample size (or number of clusters) by treatment arms

Half of the shops will be contacted using a female name and half of the shops with a male name

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

I should be able to detect a difference in prices between males and females within types of $14.7 to $21.9, given a statistical power of 80%, 1% significance level and sample size of 10,000 observed prices.

Supporting Documents and Materials

IRB